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<hr>
<h2 id="核心原理：RANSAC-与平面分割"><a href="#核心原理：RANSAC-与平面分割" class="headerlink" title="核心原理：RANSAC 与平面分割"></a>核心原理：RANSAC 与平面分割</h2><h3 id="1-什么是-RANSAC？"><a href="#1-什么是-RANSAC？" class="headerlink" title="1. 什么是 RANSAC？"></a>1. 什么是 RANSAC？</h3><p><strong>RANSAC</strong>（Random Sample Consensus，随机采样一致性）是一种鲁棒的参数估计方法，能够在包含大量 <strong>离群点（outliers）</strong> 的数据中，拟合出最可能的数学模型。</p>
<h4 id="✅-优势："><a href="#✅-优势：" class="headerlink" title="✅ 优势："></a>✅ 优势：</h4><ul>
<li>对噪声和异常值不敏感</li>
<li>适用于不完整或稀疏的数据</li>
<li>广泛用于计算机视觉、点云处理、SLAM 等领域</li>
</ul>
<hr>
<h3 id="2-平面模型定义"><a href="#2-平面模型定义" class="headerlink" title="2. 平面模型定义"></a>2. 平面模型定义</h3><p>在三维空间中，一个平面可以用如下方程表示：</p>
<p>$$<br>ax + by + cz + d &#x3D; 0<br>$$</p>
<p>其中：</p>
<ul>
<li>$ (a, b, c) $ 是平面的法向量</li>
<li>$ d $ 是原点到平面的距离（符号距离）</li>
<li>所有满足该方程且距离小于阈值的点被视为 <strong>内点（inliers）</strong></li>
</ul>
<hr>
<h3 id="3-RANSAC-平面拟合流程"><a href="#3-RANSAC-平面拟合流程" class="headerlink" title="3. RANSAC 平面拟合流程"></a>3. RANSAC 平面拟合流程</h3><ol>
<li><strong>随机采样</strong>：从点云中随机选取 3 个不共线的点，确定一个候选平面。</li>
<li><strong>模型拟合</strong>：计算这三点所确定的平面方程。</li>
<li><strong>内点统计</strong>：遍历所有点，计算其到平面的距离，若小于设定阈值，则归为内点。</li>
<li><strong>迭代优化</strong>：重复上述过程若干次，选择内点数量最多的模型作为最佳拟合。</li>
<li><strong>系数优化（可选）</strong>：对最终模型进行最小二乘优化，提高精度。</li>
</ol>
<blockquote>
<p>💡 最终输出：</p>
<ul>
<li>平面模型系数 $[a, b, c, d]$</li>
<li>内点索引列表</li>
</ul>
</blockquote>
<hr>
<h3 id="4-在-PCL-中的关键组件"><a href="#4-在-PCL-中的关键组件" class="headerlink" title="4. 在 PCL 中的关键组件"></a>4. 在 PCL 中的关键组件</h3><table>
<thead>
<tr>
<th>组件</th>
<th>作用</th>
</tr>
</thead>
<tbody><tr>
<td><code>SACMODEL_PLANE</code></td>
<td>指定拟合模型为平面</td>
</tr>
<tr>
<td><code>SAC_RANSAC</code></td>
<td>使用 RANSAC 方法进行估计</td>
</tr>
<tr>
<td><code>setDistanceThreshold()</code></td>
<td>设定点到平面的最大允许距离</td>
</tr>
<tr>
<td><code>ModelCoefficients</code></td>
<td>存储拟合出的平面参数</td>
</tr>
<tr>
<td><code>PointIndices</code></td>
<td>存储内点的索引</td>
</tr>
</tbody></table>
<hr>
<h2 id="代码实现"><a href="#代码实现" class="headerlink" title="代码实现"></a>代码实现</h2><figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;iostream&gt;</span>                    <span class="comment">// 标准输入输出流</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/ModelCoefficients.h&gt;</span>     <span class="comment">// PCL 模型系数类，用于存储平面、圆柱等几何模型参数</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/io/pcd_io.h&gt;</span>             <span class="comment">// PCL 点云文件读写（如 .pcd 文件）</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/point_types.h&gt;</span>           <span class="comment">// PCL 点类型定义（如 pcl::PointXYZ）</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/sample_consensus/method_types.h&gt;</span> <span class="comment">// 采样一致性算法类型（如 RANSAC）</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/sample_consensus/model_types.h&gt;</span>  <span class="comment">// 模型类型（如平面、球体等）</span></span></span><br><span class="line"><span class="meta">#<span class="keyword">include</span> <span class="string">&lt;pcl/segmentation/sac_segmentation.h&gt;</span> <span class="comment">// 基于采样一致性（SAC）的分割模块</span></span></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="type">int</span> <span class="title">main</span> <span class="params">(<span class="type">int</span> argc, <span class="type">char</span>** argv)</span></span></span><br><span class="line"><span class="function"></span>&#123;</span><br><span class="line">  pcl::PointCloud&lt;pcl::PointXYZ&gt; cloud; <span class="comment">// 定义一个存储 XYZ 类型点的点云对象</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 填充点云数据</span></span><br><span class="line">  cloud.width  = <span class="number">15</span>;                    <span class="comment">// 设置点云宽度为 15（表示有 15 个有序点）</span></span><br><span class="line">  cloud.height = <span class="number">1</span>;                     <span class="comment">// 高度为 1，表示这是一个无序点云（总共 15×1=15 个点）</span></span><br><span class="line">  cloud.points.<span class="built_in">resize</span> (cloud.width * cloud.height); <span class="comment">// 分配内存空间给点数组</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 生成数据</span></span><br><span class="line">  <span class="keyword">for</span> (<span class="type">size_t</span> i = <span class="number">0</span>; i &lt; cloud.points.<span class="built_in">size</span> (); ++i) <span class="comment">// 遍历每个点</span></span><br><span class="line">  &#123;</span><br><span class="line">    cloud.points[i].x = <span class="number">1024</span> * <span class="built_in">rand</span> () / (RAND_MAX + <span class="number">1.0f</span>); <span class="comment">// 随机生成 x 坐标（0~1024）</span></span><br><span class="line">    cloud.points[i].y = <span class="number">1024</span> * <span class="built_in">rand</span> () / (RAND_MAX + <span class="number">1.0f</span>); <span class="comment">// 随机生成 y 坐标（0~1024）</span></span><br><span class="line">    cloud.points[i].z = <span class="number">1.0</span>;                                <span class="comment">// 所有点 z 值初始设为 1.0</span></span><br><span class="line">  &#125;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 设置几个局外点（outliers），用于测试 RANSAC 的鲁棒性</span></span><br><span class="line">  cloud.points[<span class="number">0</span>].z = <span class="number">2.0</span>;  <span class="comment">// 第0个点 z 改为 2.0，偏离平面</span></span><br><span class="line">  cloud.points[<span class="number">3</span>].z = <span class="number">-2.0</span>; <span class="comment">// 第3个点 z 改为 -2.0，偏离平面</span></span><br><span class="line">  cloud.points[<span class="number">6</span>].z = <span class="number">4.0</span>;  <span class="comment">// 第6个点 z 改为 4.0，偏离平面</span></span><br><span class="line"></span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;Point cloud data: &quot;</span> &lt;&lt; cloud.points.<span class="built_in">size</span> () &lt;&lt;<span class="string">&quot; points&quot;</span> &lt;&lt; std::endl; <span class="comment">// 输出点云总数</span></span><br><span class="line">  <span class="keyword">for</span> (<span class="type">size_t</span> i = <span class="number">0</span>; i &lt; cloud.points.<span class="built_in">size</span> (); ++i)</span><br><span class="line">    std::cerr &lt;&lt; <span class="string">&quot;    &quot;</span> &lt;&lt; cloud.points[i].x &lt;&lt; <span class="string">&quot; &quot;</span> </span><br><span class="line">              &lt;&lt; cloud.points[i].y &lt;&lt; <span class="string">&quot; &quot;</span> </span><br><span class="line">              &lt;&lt; cloud.points[i].z &lt;&lt; std::endl; <span class="comment">// 打印每个点的坐标</span></span><br><span class="line"></span><br><span class="line">  <span class="comment">// 创建模型系数指针，用于存储拟合出的平面方程参数（ax + by + cz + d = 0）</span></span><br><span class="line">  pcl::<span class="function">ModelCoefficients::Ptr <span class="title">coefficients</span> <span class="params">(<span class="keyword">new</span> pcl::ModelCoefficients)</span></span>;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 创建内点索引指针，用于存储符合模型的点的索引</span></span><br><span class="line">  pcl::<span class="function">PointIndices::Ptr <span class="title">inliers</span> <span class="params">(<span class="keyword">new</span> pcl::PointIndices)</span></span>;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 创建 SACSegmentation 分割对象，模板类型为 pcl::PointXYZ</span></span><br><span class="line">  pcl::SACSegmentation&lt;pcl::PointXYZ&gt; seg;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 可选设置：启用系数优化（使拟合结果更精确）</span></span><br><span class="line">  seg.<span class="built_in">setOptimizeCoefficients</span> (<span class="literal">true</span>);</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 必须设置：设定要拟合的模型类型为平面（ax + by + cz + d = 0）</span></span><br><span class="line">  seg.<span class="built_in">setModelType</span> (pcl::SACMODEL_PLANE);</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 必须设置：设定使用的方法为 RANSAC（随机抽样一致性算法）</span></span><br><span class="line">  seg.<span class="built_in">setMethodType</span> (pcl::SAC_RANSAC);</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 必须设置：设定距离阈值，点到平面的距离小于该值则认为是内点</span></span><br><span class="line">  seg.<span class="built_in">setDistanceThreshold</span> (<span class="number">0.01</span>);</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 必须设置：输入待处理的点云数据（makeShared() 创建共享指针）</span></span><br><span class="line">  seg.<span class="built_in">setInputCloud</span> (cloud.<span class="built_in">makeShared</span> ());</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 执行分割：输出内点索引和模型系数</span></span><br><span class="line">  seg.<span class="built_in">segment</span> (*inliers, *coefficients);</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 如果没有找到足够内点，则说明平面拟合失败</span></span><br><span class="line">  <span class="keyword">if</span> (inliers-&gt;indices.<span class="built_in">size</span> () == <span class="number">0</span>)</span><br><span class="line">  &#123;</span><br><span class="line">    <span class="built_in">PCL_ERROR</span> (<span class="string">&quot;Could not estimate a planar model for the given dataset.&quot;</span>); <span class="comment">// 输出错误信息</span></span><br><span class="line">    <span class="keyword">return</span> (<span class="number">-1</span>); <span class="comment">// 返回错误码</span></span><br><span class="line">  &#125;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 输出拟合得到的平面模型系数（a, b, c, d），对应平面方程 ax+by+cz+d=0</span></span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;Model coefficients: &quot;</span> &lt;&lt; coefficients-&gt;values[<span class="number">0</span>] &lt;&lt; <span class="string">&quot; &quot;</span> </span><br><span class="line">            &lt;&lt; coefficients-&gt;values[<span class="number">1</span>] &lt;&lt; <span class="string">&quot; &quot;</span></span><br><span class="line">            &lt;&lt; coefficients-&gt;values[<span class="number">2</span>] &lt;&lt; <span class="string">&quot; &quot;</span> </span><br><span class="line">            &lt;&lt; coefficients-&gt;values[<span class="number">3</span>] &lt;&lt; std::endl;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 输出内点数量</span></span><br><span class="line">  std::cerr &lt;&lt; <span class="string">&quot;Model inliers: &quot;</span> &lt;&lt; inliers-&gt;indices.<span class="built_in">size</span> () &lt;&lt; std::endl;</span><br><span class="line"></span><br><span class="line">  <span class="comment">// 遍历并打印所有内点的索引及其坐标</span></span><br><span class="line">  <span class="keyword">for</span> (<span class="type">size_t</span> i = <span class="number">0</span>; i &lt; inliers-&gt;indices.<span class="built_in">size</span> (); ++i)</span><br><span class="line">    std::cerr &lt;&lt; inliers-&gt;indices[i] &lt;&lt; <span class="string">&quot;    &quot;</span> &lt;&lt; cloud.points[inliers-&gt;indices[i]].x &lt;&lt; <span class="string">&quot; &quot;</span></span><br><span class="line">              &lt;&lt; cloud.points[inliers-&gt;indices[i]].y &lt;&lt; <span class="string">&quot; &quot;</span></span><br><span class="line">              &lt;&lt; cloud.points[inliers-&gt;indices[i]].z &lt;&lt; std::endl;</span><br><span class="line"></span><br><span class="line">  <span class="keyword">return</span> (<span class="number">0</span>); <span class="comment">// 正常退出程序</span></span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>

<h2 id="终端输出例如"><a href="#终端输出例如" class="headerlink" title="终端输出例如"></a>终端输出例如</h2><figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br></pre></td><td class="code"><pre><span class="line">Point cloud data: 15 points</span><br><span class="line">    0.352222 -0.151883 2</span><br><span class="line">    -0.106395 -0.397406 1</span><br><span class="line">    -0.473106 0.292602 1</span><br><span class="line">    -0.731898 0.667105 -2</span><br><span class="line">    0.441304 -0.734766 1</span><br><span class="line">    0.854581 -0.0361733 1</span><br><span class="line">    -0.4607 -0.277468 4</span><br><span class="line">    -0.916762 0.183749 1</span><br><span class="line">    0.968809 0.512055 1</span><br><span class="line">    -0.998983 -0.463871 1</span><br><span class="line">    0.691785 0.716053 1</span><br><span class="line">    0.525135 -0.523004 1</span><br><span class="line">    0.439387 0.56706 1</span><br><span class="line">    0.905417 -0.579787 1</span><br><span class="line">    0.898706 -0.504929 1</span><br><span class="line">Model coefficients: 0 0 1 -1</span><br><span class="line">Model inliers: 12</span><br><span class="line">1    -0.106395 -0.397406 1</span><br><span class="line">2    -0.473106 0.292602 1</span><br><span class="line">4    0.441304 -0.734766 1</span><br><span class="line">5    0.854581 -0.0361733 1</span><br><span class="line">7    -0.916762 0.183749 1</span><br><span class="line">8    0.968809 0.512055 1</span><br><span class="line">9    -0.998983 -0.463871 1</span><br><span class="line">10    0.691785 0.716053 1</span><br><span class="line">11    0.525135 -0.523004 1</span><br><span class="line">12    0.439387 0.56706 1</span><br><span class="line">13    0.905417 -0.579787 1</span><br><span class="line">14    0.898706 -0.504929 1</span><br></pre></td></tr></table></figure></article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span 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href="#%E5%9F%BA%E4%BA%8E-RANSAC-%E7%9A%84%E7%82%B9%E4%BA%91%E5%B9%B3%E9%9D%A2%E5%88%86%E5%89%B2%E5%8E%9F%E7%90%86%E4%B8%8E%E5%AE%9E%E7%8E%B0"><span class="toc-number">1.</span> <span class="toc-text">基于 RANSAC 的点云平面分割原理与实现</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E6%A0%B8%E5%BF%83%E5%8E%9F%E7%90%86%EF%BC%9ARANSAC-%E4%B8%8E%E5%B9%B3%E9%9D%A2%E5%88%86%E5%89%B2"><span class="toc-number">1.1.</span> <span class="toc-text">核心原理：RANSAC 与平面分割</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#1-%E4%BB%80%E4%B9%88%E6%98%AF-RANSAC%EF%BC%9F"><span class="toc-number">1.1.1.</span> <span class="toc-text">1. 什么是 RANSAC？</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E2%9C%85-%E4%BC%98%E5%8A%BF%EF%BC%9A"><span class="toc-number">1.1.1.1.</span> <span class="toc-text">✅ 优势：</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#2-%E5%B9%B3%E9%9D%A2%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89"><span class="toc-number">1.1.2.</span> <span class="toc-text">2. 平面模型定义</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#3-RANSAC-%E5%B9%B3%E9%9D%A2%E6%8B%9F%E5%90%88%E6%B5%81%E7%A8%8B"><span class="toc-number">1.1.3.</span> <span class="toc-text">3. RANSAC 平面拟合流程</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#4-%E5%9C%A8-PCL-%E4%B8%AD%E7%9A%84%E5%85%B3%E9%94%AE%E7%BB%84%E4%BB%B6"><span class="toc-number">1.1.4.</span> <span class="toc-text">4. 在 PCL 中的关键组件</span></a></li></ol></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0"><span class="toc-number">1.2.</span> <span class="toc-text">代码实现</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%BB%88%E7%AB%AF%E8%BE%93%E5%87%BA%E4%BE%8B%E5%A6%82"><span class="toc-number">1.3.</span> <span class="toc-text">终端输出例如</span></a></li></ol></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2025/10/05/2025-10-05-lio_sam/" title="lio_sam">lio_sam</a><time datetime="2025-10-05T02:43:51.000Z" title="发表于 2025-10-05 10:43:51">2025-10-05</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2025/08/12/2025-08-12-%E4%BD%BF%E7%94%A8docker%E6%90%AD%E5%BB%BApytorch%E7%8E%AF%E5%A2%83/" title="使用docker搭建pytorch环境">使用docker搭建pytorch环境</a><time datetime="2025-08-12T12:43:33.000Z" title="发表于 2025-08-12 20:43:33">2025-08-12</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2025/08/05/2025-08-05-docker/" title="docker">docker</a><time datetime="2025-08-05T11:02:36.000Z" title="发表于 2025-08-05 19:02:36">2025-08-05</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2025/08/05/2025-08-05-%E8%BF%90%E5%8A%A8%E5%AF%B9%E8%B1%A1%E5%88%86%E5%89%B2%E4%B8%8E%E9%85%8D%E5%87%86%E7%AE%97%E6%B3%95/" title="运动对象分割与配准算法">运动对象分割与配准算法</a><time datetime="2025-08-05T08:16:02.000Z" title="发表于 2025-08-05 16:16:02">2025-08-05</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2025/08/05/2025-08-05-%E6%9D%A1%E4%BB%B6%E6%AC%A7%E5%BC%8F%E8%81%9A%E7%B1%BB%E5%88%86%E5%89%B2/" title="条件欧式聚类分割">条件欧式聚类分割</a><time datetime="2025-08-05T07:56:39.000Z" title="发表于 2025-08-05 15:56:39">2025-08-05</time></div></div></div></div></div></div></main><footer id="footer"><div class="footer-other"><div class="footer-copyright"><span class="copyright">&copy;&nbsp;2025 By Fi9zero</span><span class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo 7.3.0</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly 5.4.2</a></span></div><div class="footer_custom_text">Only light can attract bugs.</div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="readmode" type="button" title="阅读模式"><i class="fas fa-book-open"></i></button><button id="darkmode" type="button" title="日间和夜间模式切换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button class="close" id="mobile-toc-button" type="button" title="目录"><i class="fas fa-list-ul"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js"></script><script src="/js/main.js"></script><div class="js-pjax"><script>(() => {
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